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# Senior ML Engineer (Data Scientist)

- **Company:** CLERA, LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Extract Transform Load (ETL), Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, NoSQL, Tensorflow, SQL Databases, Cloud Platform System, Data Ingestion, Pytorch, Apache Spark, Containerization, Kubernetes, Dask, Machine Learning Operations, Data Pipelines, Docker, Data Generation
- **Published:** August 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a680bbd22cb24e19

## About the Role

* 3+ years of experience as a Data Scientist or Machine Learning Engineer.
 * Hands-on experience building and deploying ML models with PyTorch and TensorFlow; strong proficiency in Python.
 * Strong ML/DS fundamentals with the ability to translate research insights into product decisions., * Experience with distributed data processing frameworks such as Spark, Dask, or Ray.
 * Proficiency with containerization and orchestration tools - Docker and Kubernetes.
 * Proven experience deploying data science and ML workloads on cloud platforms (AWS, GCP, and/or Azure).

Nice to have:

 * Experience designing and implementing scalable data pipelines and experimentation frameworks.
 * Background in causal inference, synthetic data generation, or behavioral modeling.
 * Familiarity with model monitoring, drift detection, and explainability tooling.

## Description

We're a Series A MLOps and enterprise AI platform company helping organizations deploy, manage, and monitor machine learning models at scale. Our Kubernetes-native infrastructure and model governance tooling are trusted by enterprise customers, and we're now investing heavily in predictive product simulations, agentic AI capabilities, and next-generation data science infrastructure., * Build and optimize data pipelines (ETL/ELT) across SQL/NoSQL systems, ensuring reliability and quality of large-scale event and log data.

 * Apply statistical modeling, causal inference, and ML to analyze user behavior, design experiments, and generate actionable insights.
 * Develop predictive, generative, and clustering models - including embeddings, anomaly detection, and time-series - to power simulations and personalization features.
 * Collaborate with a multidisciplinary team of GenAI experts, behavioral scientists, and ML engineers to create synthetic personas and deliver customer-ready reports and presentations.
 * Deploy and scale models in cloud environments (AWS, GCP, and/or Azure) using containerized workflows with Docker and Kubernetes.
 * Design and maintain monitoring and evaluation pipelines to track model performance, detect drift, and ensure fairness and reproducibility.
 * Scale data science infrastructure end-to-end - from ingestion pipelines through to experimentation frameworks.


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